Sensitive Method for Detection and Quantification of Anthrax, Bordetella pertussis, Clostridium difficile, Clostridium botulinum and Other Pathogen-Derived Toxins in Human and Animal Plasma

CDC research scientists have developed a method to identify and quantify the activity of pathogenic bacterial adenylate cyclase toxins by liquid chromatography tandem mass spectrometry (LC-MS/MS). Bacterial protein toxins are among the most potent natural poisons known, causing paralysis, immune system collapse, hemorrhaging and death in some cases.

An Automated System for Myocardial Perfusion Mapping and Machine Diagnosis to Detect Ischemic Heart Disease with First-pass Perfusion Cardiac Magnetic Resonance Imaging

This technology includes a fully automated computer aided diagnosis system to quantify myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) pixel maps from the first-pass contrast-enhanced cardiac magnetic resonance (CMR) perfusion images. This system performs automated image registration, motion compensation, segmentation, and modeling to extract quantitative features from different myocardial regions of interest.

Anti-Puromycin Antibodies Illuminate the World of Cellular Protein Translation

The Ribopuromycylation (RPM) technology, developed by Dr. Jon Yewdell and Dr. Alexandre David, offers a powerful and universal method for visualizing and studying protein translation within cells. RPM involves the use of puromycin, a molecule that mimics a tyrosyl-tRNA and terminates translation by becoming covalently incorporated into the nascent protein chain's C-terminus within the ribosome's A site. This technique enables the immobilization of puromycylated nascent protein chains on ribosomes when chain elongation inhibitors like cycloheximide or emetine are utilized.

System for Automated Anatomical Structures Segmentation of Contrast-Enhanced Cardiac Computed Tomography Images

This technology includes a fully automatic 3D image processing system to segment the heart as well as other organs from contrast-enhanced cardiac computed tomography (CCT) images. Our method detects four cardiac chambers including left ventricle, right ventricle, left atrium, right atrium, as well as the ascending aorta and left ventricular myocardium. It also classifies noncardiac tissue structures in the CCT images such as lung, chest wall, spine, descending aorta, and liver.

General-purpose Deep Learning Image Denoising Based on Magnetic Resonance Imaging Physics

This technology includes a novel method to train deep learning convolution neural network model to improve the signal-noise-ratio for the magnetic resonance (MR) imaging. The novelty lies on the fact that actual MR imaging physics information is used in the deep learning training. The resulting model achieves significant signal-to-noise ratio (SNR) improved for different acceleration factors in MR imaging. The resulting model can be used for many body anatomies (e.g., brain, heart, liver, spine, etc.) to significantly improve the SNR.

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